To achieve an automatic and smart monitoring of the production processes, by detecting at runtime anomalies and occurring deviations, raising elements for better and new insight to production to DISRUPT platform


Advanced functionalities for system identification – coming from machine learning – capable of detecting event patterns in real time leveraging statistical inference and identifying their evolution, raising messages on the DISRUPT bus with forecast on future occurrences of events at shopfloor. The core solution is a flexible python application based on statistical open-source libraries, conveying data and events in inboud/outboud through a lightweight IoT connectivity protocol. In addition to the DISRUPT platform, an intuitive user interface allows real time monitoring of the shopfloor in order to provide a better understanding of the production processes, their current state and their evolution over time.


Increased early assessment of events impact and advanced prediction on complex events at plant/supply chain level

TRL: 5




This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 723541

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